(III). Activation function (Transfer function): In a neural network based on the weighted sum of the neuronal input activation function transforms it into output form. It performs mapping of the input to the output depending upon the neuronal input so as to fire a particular neuron or not. Activation functions can be linear or non-linear functions. Some of the activation function used in CNN are described below:
(a)Rectilinear Unit (ReLU): The ReLU function converts the input to a piecewise linear function to a positive output otherwise it will output zero. It is one of the common activation functions in most of the neural networks. One of the advantages of using ReLU over other activation functions is that it has lower computational load[1]}. Mathematically it is represented as below,
\(f(x)_{ReLU}=max\lbrace 0,x\rbrace \) 
